Co-EM Support Vector learning
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Co-EM Support Vector learning. / Brefeld, Ulf; Scheffer, Tobias.
Proceeding ICML '04 Proceedings of the twenty-first international conference on Machine learning. New York : Association for Computing Machinery, Inc, 2004. p. 121-128 (Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Co-EM Support Vector learning
AU - Brefeld, Ulf
AU - Scheffer, Tobias
N1 - Conference code: 21
PY - 2004
Y1 - 2004
N2 - Multi-view algorithms, such as co-training and co-EM, utilize unlabeled data when the available attributes can be split into independent and compatible subsets. Co-EM outperforms co-training for many problems, but it requires the underlying learner to estimate class probabilities, and to learn from probabilistically labeled data. Therefore, co-EM has so far only been studied with naive Bayesian learners. We cast linear classifiers into a probabilistic framework and develop a co-EM version of the Support Vector Machine. We conduct experiments on text classification problems and compare the family of semi-supervised support vector algorithms under different conditions, including violations of the assumptions underlying multiview learning. For some problems, such as course web page classification, we observe the most accurate results reported so far.
AB - Multi-view algorithms, such as co-training and co-EM, utilize unlabeled data when the available attributes can be split into independent and compatible subsets. Co-EM outperforms co-training for many problems, but it requires the underlying learner to estimate class probabilities, and to learn from probabilistically labeled data. Therefore, co-EM has so far only been studied with naive Bayesian learners. We cast linear classifiers into a probabilistic framework and develop a co-EM version of the Support Vector Machine. We conduct experiments on text classification problems and compare the family of semi-supervised support vector algorithms under different conditions, including violations of the assumptions underlying multiview learning. For some problems, such as course web page classification, we observe the most accurate results reported so far.
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=14344251008&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/464ca35f-43df-33f6-acdc-c9ae6911d1a7/
U2 - 10.1145/1015330.1015350
DO - 10.1145/1015330.1015350
M3 - Article in conference proceedings
AN - SCOPUS:14344251008
SN - 1-58113-838-5
SN - 978-1-58113-838-2
T3 - Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
SP - 121
EP - 128
BT - Proceeding ICML '04 Proceedings of the twenty-first international conference on Machine learning
PB - Association for Computing Machinery, Inc
CY - New York
T2 - 21st International Conference on Machine Learning - 2004
Y2 - 31 December 2004
ER -